Daniel

domingo, 12 de agosto de 2012

Social Network

Social networks are social structures composed of groups of people, which are connected by one or more types of relationships such as friendship, kinship, or who share common interests conocimientos.Para online communication platforms,
Social network analysis examines the social structure using Graph Theory and identifying the entities as "nodes" or "vertices" and relationships as "links" or "edges". The structure of the resulting graph is often very complex. As mentioned, there may be many types of links between nodes. The multidisciplinary research has shown that social networks operate on many levels, from family relationships to relationships statewide organizations (in this case we speak of political networks), playing a critical role in determining the political agenda and the degree to which individuals or organizations achieve their objectives or are influenced.
In its simplest form, a social network is a map of all the relevant links between all nodes studied. We speak here of networks "sociocentric" or "complete". Another option is to identify the network that involves a person (in different social contexts in which they interact), in which case we speak of "personal network".
The social network can also be used to measure social capital (ie the value that an individual obtains accessible resources through their social network). These concepts are shown, often in a diagram where the nodes are points and loops, lines.

Social network analysis

Example of a social network diagram. The node with the highest centrality intermediation is marked in yellow.
The social network analysis (related to network theory) has emerged as a key approach in the modern social sciences, among which include sociology, anthropology, social psychology, economics, geography, political science, scientometrics, communication studies, organizational studies and sociolinguistics. He has also won significant support in physics and biology among others.
In everyday language is freely used the idea of ​​"social network" for over a century to denote complex sets of relationships between members of social systems in all dimensions, from the interpersonal to the international level. In 1954, anthropologist J. Manchester School A. Barnes started using the term systematically to show patterns of loops, covering the concepts traditionally used by social scientists: bounded groups (eg, tribes, families) and social categories (eg, gender, ethnicity). Academic and S.D. Berkowitz, Stephen Borgatti, Ronald Burt Burt, Kathleen Carley Carley, Martin Everett, Katherine Faust, Linton Freeman, Mark Granovetter Granovetter, David Knoke, David Krackhardt Krackhardt, Peter Marsden, Nicholas Mullins, Anatol Rapoport Rapoport, Stanley Wasserman, Barry Wellman Wellman R. Douglas R. White White, White and Harrison White expanded the use of systematic social network analysis.
Analysis of social networks has grown from a suggestive metaphor to become an analytical approach and a paradigm, with its theoretical principles, methods, software network analysis software for analysis of social networks and lines of research themselves. Analysts studying the influence of all the parts and vice versa, the effect of the selective action of individuals in the network, from structure to the relationship and the individual, from behavior to attitude. As mentioned these tests are done well in complete networks, where ties are specific relationships in a defined population, or in personal networks (also known as egocentric networks, while not exactly comparable), where we study "personal communities "2 The distinction between networks / total and complete personal networks / egocentric depends much more on the analyst's ability to collect data and information. That is, for groups such as businesses, schools or companies with membership, the analyst expects to have full information on who is on the network, all of egos and alters the potential participants. The personal study / self-centered are usually conducted when the identities or egos known, but not their otherness. These studies allow egos to provide information on the identity of their alters and there is no expectation that different sets of egos or alters are linked to each other.

Another schematic representation of a social network.
A network built from a snowball refers to the idea that the alters are identified in a survey by a set of initial Egos (zero tide) and the same alters egos become in wave 1 and appoint other Additional alters and so on until the percentage of new alters begins to decrease. Although there are several logistical limitations in conducting studies snowball recent development is to consider hybrid networks, in which egos in complete networks can nominate alters who would not otherwise identified, allowing them to be visible to all red.3 egos of the hybrid network can be valuable to examine total networks / complete on the expectation that there are major players include beyond the formally identified. For example, company employees often work with outside consultants who are part of a network that can not be fully defined before data collection.
In social network analysis, presents several analytical trends:

No part of the hypothesis that the blocks are groups in society: the approach is open to studying less defined social systems, from nonlocal communities to links through websites.

Rather than treating individuals (persons, organizations, states) as discrete units of analysis focuses on how the structure of relations affects individuals and their relationships.

In contrast to analyzes that assume that socialization of norms determines behavior, network analysis is used to observe the extent to which the structure and composition of relations between individuals affect the rules.

The shape of a social network helps determine the usefulness of the network for its members. Smaller networks and more stringent, may be less useful to their members than networks with lots of loose connections (weak link) with people outside the main network. The more open networks with many weak ties and social relations are more likely to present new ideas and opportunities to their members than closed networks with many redundant ties. In other words, a group of friends who only do things with each other and share the same knowledge and opportunities. A group of individuals with connections to other social worlds is likely to have access to a wider range of information. It is better for individual success to have connections with a variety of networks rather than many connections within a single network. Similarly, individuals can exercise influence or act as middlemen, in their social networks, a bridge between two networks that are not directly linked (called filling structural holes).

The power of social network analysis is its difference from traditional studies in the social sciences, which assume that the attributes of individual actors-whether friendly or unfriendly, smart or dumb, etc.-is what matters. Social network analysis produces a vision that is both complementary and alternative, in which the attributes of individuals are less important than relationships and linkages with other actors within the network. This approach has proved useful for explaining many real-world phenomena, but leaves less room for individual action and the ability of people to influence their success as largely based on the structure of your network.

Social networks have also been used to examine how organizations interact with each other, characterizing the many informal connections that link executives together, as well as associations and connections between employees of different organizations. For example, power within organizations often comes more from the degree to which an individual within a network is in the center of many relationships, your real job. Social networks also play a key role in recruitment within the commercial success and job performance. Networks are ways in which companies collect information, discourage competition and collusion in setting prices or policies.

Linton Freeman has written the history of the progress of social networks and social network analysis.

Precursors of social networks in the late eighteenth century include Émile Durkheim and Ferdinand Tönnies. Tönnies argued that social groups can exist either as personal and direct social ties that link individuals who share those values ​​and belief (gemeinschaft), or as formal and instrumental social links (gesellschaft). Durkheim provided no explanation individualistic social fact, arguing that social phenomena arise when interacting individuals constitute a reality that can not be explained in terms of the attributes of individual actors. Distinction made between a traditional society, with "mechanical solidarity" - which prevails if individual differences are minimized, and a modern society with "organic solidarity" - which develops cooperation between differentiated individuals with independent roles.

Meanwhile, Georg Simmel in the early twentieth century, was the first scholar to think directly in terms of social network. Their tests indicate the nature of the network size and the interaction probability of interaction in branched networks of weak point, rather than in groups. (Simmel, 1908/1971).

After a pause in the first decades of the twentieth century, there were three main traditions in social networks. In the 1930s, L. Moreno J.L. Moreno pioneered the systematic recording and analysis of social interaction in small groups, especially classrooms and work groups (sociometry), while a Harvard group led by W. Lloyd Warner Lloyd Warner and Elton Mayo explored May interpersonal relationships at work. In 1940, in his address to British anthropologists, AR Radcliffe-Brown urged the systematic study of networks.8 However, it took about 15 years before this call was followed systematically.

The Social network analysis developed with the kinship studies of Elizabeth Bott in England between 1950 and the studies of urbanization of the group of anthropologists from the University of Manchester (alongside Max Gluckman and later J. Clyde Mitchell Clyde Mitchell) between 1950 and 1960, investigating community networks in southern Africa, India and the United Kingdom. At the same time, British anthropologist SF Nadel codified Frederick Nadel a theory of social structure that later affected the network analysis.

Between 1960 and 1970, a growing number of scholars worked on the combination of different themes and traditions. One group was the Whitey Harrison White students in the Department of Social Relations at Harvard University: Ivan Chase, Bonnie Erickson, Harriet Friedmann, Mark Granovetter Granovetter, Nancy Howell, Joel Levine, Nicholas Mullins, John Padgett, Schwartz (sociologist ) Michael Schwartz and Barry Wellman Wellman. Other important people in this initial group were Charles Tilly, who focused on networks in political sociology and social movements, and Stanley Milgram, who developed the theory of "six degrees of separation." Mark Granovetter and Barry Wellman are among the former students of White who have elaborated and popularized social network analysis.

But the White group was not alone. Elsewhere, other scholars have developed a significant independent work: social scientists interested in mathematical applications, University of California Irvine around Linton Freeman, including John Boyd, Susan Freeman, Kathryn Faust, A. Kimball Romney Kimball Romney and Douglas White White, quantitative analyst at the University of Chicago, including Joseph Galaskiewicz, Wendy Griswold, Edward Laumann, Peter Marsden, Martina Morris, and John Padgett, and communication scholars at the University of Michigan, including Lin Nan Lin and Everett Rogers Rogers. In the 70s, it was a group-oriented substantive sociology at the University of Toronto, about former students of Harrison White: SD Berkowitz, Harriet Friedmann, Nancy Leslie Howard, Nancy Howell, Lorne Tepperman and Barry Wellman Wellman, and also accompanied the noted theoretical modeller and the Anatol Rapoport Rapoport games. In terms of theory, criticized methodological individualism and group-based analysis, arguing that see the world from the perspective of social networks provides a more analytical leverage.

In the world there are Latin American magazine and web NETWORKS NETWORKS, housed in RED IRIS, created from the International Social Network held in Sitges, Barcelona, ​​in 1998.

Research

Social network analysis has been used in epidemiology to help understand how patterns of human contact help or hinder the spread of diseases such as HIV in a population. The evolution of social networks can sometimes be simulated by the use of agent-based models, providing insight into the interplay between communication rules, rumor spreading and social structure.

Social network analysis can also be an effective tool for mass surveillance - for example, the Information Awareness Total Information Awareness conducted a thorough investigation on strategies to analyze social networks to determine whether U.S. citizens or not they were political threats.

The Diffusion of innovations theory explores social networks and their role in influencing the spread of new ideas and practices. The change agents and opinion leader often have a greater role in encouraging the adoption of innovations, although it also involves factors inherent to innovation.

Meanwhile, Dunbar Robin Dunbar suggested that the typical measure in a egocentric network is limited to about 150 members due to the possible limits of the channel capacity of human communication. This rule arises from cross-cultural studies of sociology and anthropology, especially on the maximum size of a village (in modern language best understood as an eco village). This is theorized in evolutionary psychology when he says that the number may be a lucky number limit or average human ability to recognize members and continue to emotional events with all members of a group. However, this may be due to the intervention of the economy and the need to follow the "free riders", which makes it easier in large groups to take advantage of the benefits of living in a community without contributing to those benefits.

Granovetter Mark Granovetter found in a study that a large number of weak ties can be important for finding information and innovation. The Cliques have a tendency to more homogeneous opinions and to share many common features. This trend is why hemophilia which members of the cliques are drawn first. However, similarly, each member of the clique also knows about what others know. To find new information or ideas, clique members will have to look beyond this to your other friends and acquaintances. This is what Granovetter called "strength of weak ties."

There are other uses of the term social network. For example, Guanxi is a central concept in Chinese society (and other East Asian cultures), which can be summarized as the use of personal influence. The Guanxi can be studied from a social network approach.

The small world phenomenon is the hypothesis that the chain of social acquaintances required to connect to an arbitrary person to another arbitrary person anywhere in the world, is usually short. The concept led to the famous phrase six degrees of separation from the results of "a small world experiment" made in 1967 by psychologist Stanley Milgram. In Milgram's experiment, a sample of U.S. individuals was asked to do to get a message to a particular target person by passing it along a chain of acquaintances. The average length of successful chains turned out to be about five intermediaries or six separation steps (most of the strings in this study and are not complete). The methods (and ethics as well) of Milgram's experiment was later questioned by an American scholar, and some other research to replicate Milgram's findings have found that the degrees of connection needed could be higher. Academic researchers continue to explore this phenomenon as the technology of Internet-based communication has completed the telephone and postal systems available in times of Milgram. A recent electronic small world experiment at Columbia University, found that about five to seven degrees of separation are sufficient to connect any two people through e-mail.

Collaboration graphs can be used to illustrate good and bad relationships between humans. A positive link between two nodes denotes a positive relationship (friendship, alliance, dating) and a negative link between two nodes denotes a negative relationship (hatred, anger). These graphs of social networks can be used to predict the future evolution of the graph. In them, there is the concept of cycles "balanced" and "unbalanced". An equilibrium cycle is defined as one where the product of all signs are positive. Balanced graphs represent a group of people with little chance of change in their views on others in the group. Unbalanced graphs represent a group of individual who is likely to change their views on others in their group. For example, in a group of 3 people (A, B and C) where A and B have a positive, B and C have a positive, but C and A have a negative relationship, is a cycle of imbalance. This group is very likely to become a balanced cycle, such that B only has a good relationship with A, and both A and B have a negative relationship with C. By using the concept of balanced and unbalanced cycles, can predict the evolution of the evolution of a social network graph.

A study has found that happiness tends to correlate with social networks. When a person is happy, close friends have a 25 percent chance of also being more happy. Also, people in the center of a social network tend to be happier in the future than those at the periphery. In the networks studied were observed in both groups of happy people and groups of people unhappy, with a range of three degrees of separation: the happiness of a person associated with the level of happiness of friends of friends of friends.

Some researchers have suggested that human social networks may have a genetic basis. Using a sample of twins from the Longitudinal Study of Adolescent Health National Longitudinal Study of Adolescent Health, found that the in-degree (number of times a person is named as a friend), transitivity (the likelihood that two friends are friends of another), and brokerage and centrality (the number of loops in the network that pass through a given person) are significantly heritable. Existing models of network formation can not account for this intrinsic variation, so the researchers propose an alternative model "Attracting and Present", which can explain the hereditary and many other features of human social networks.

Social networking

The germinal software social networking part of the theory of Six degrees of separation, according to which all people of the planet is connected by no more than six people. In fact, there is a U.S. patent known as six degrees patentpor who have already paid the Tribe and LinkedIn. There are many other patents covering the technology to automate the creation of networks and applications related to these.

These social networks are based on the theory of six degrees, six degrees of separation is the theory that anyone on Earth can be connected to any other person on the planet through a chain of acquaintances that has no more than six intermediaries . The theory was first proposed in 1929 by the Hungarian writer Frigyes Karinthy on a short story called Chains. The concept is based on the idea that the number of acquaintances grows exponentially with the number of links in the chain, and only a small number of links are necessary for all known becomes the entire human population.

The term social network is coined mainly British anthropologists John Barnes and Elizabeth Bott, because for them it was essential to consider external ties to family, residential or social group membership.

The goals that motivated the creation of social networking sites are several, mainly, is to design a virtual interaction, in which millions of people around the world are focused with different interests.

Also reflected in the book "Six Degrees: The Science of a Connected Age" of the sociologist Duncan Watts, and ensures that you can access anyone in the world in just six "hops".

According to this theory, each person knows on average, between friends, family and co-workers or school, about 100 people. If each of those friends or close acquaintances is related to another 100 people, anyone can pass a message to 10,000 people just asking a friend to pass the message to your friends.

These 10,000 individuals would contact second level, an individual does not know but you can easily learn by asking friends and family to the present, and that is often used to hold a job or make a purchase. When we asked someone, for example, if you know a secretary interested in working are pulling these informal social networks that run our society. This argument assumes that the 100 friends of each person are not mutual friends. In practice, this means that the number of contacts of the second level will be substantially less than 10,000 because it is very usual to have mutual friends in social networks.

If those 10,000 known another 100, the network would be expanded to 1,000,000 people connected in a third, to 100,000,000 in fourth level, fifth level in 10,000,000,000 1,000,000,000,000 and a sixth level. In six steps, and technologies available, it could send an individual anywhere in the world.

Obviously the more steps you have to give, more distant will be the connection between two individuals and communication more difficult. Internet, however, has eliminated some of these barriers creating real world social networks, especially in specific segments of professionals, artists, etc..

In the early 50's, Ithiel de Sola Pool (MIT) and Manfred Kochen (IBM) set out to prove the theory mathematically. Although they were able to state the question "given a set of N people, what is the probability that each member of these N are connected to another member via k1, k2, k3, ..., kn links?" After twenty years were still unable to resolve the problem to your own satisfaction.

In 1967, American psychologist Stanley Milgram devised a new way to test the theory, which he called "the small world problem." The small-world experiment of Milgram involved the random selection of several people from the Midwest to send postcards to a stranger located in Massachusetts, located thousands of miles away. The senders knew the recipient's name, occupation, and the approximate location. They were told to send the package to a person they know directly and to think it was the most likely would, of all his friends, to know directly to the recipient. This person would do the same and so on until the package was personally delivered to their final destination.

Although the participants expected the chain include at least hundreds of intermediaries, the delivery of each package only took, on average, between five and seven intermediaries. Milgram's findings were published in "Psychology Today" and inspired the phrase six degrees of separation.

In The social software weblog grouped 120 websites in 10 categories, and QuickBase has also developed a complete picture on social networking.

The origin of social networks goes back at least to 1995, when Randy Conrads creates the classmates.com website. This social network is intended for people to regain or maintain contact with former classmates, college, etc..

In 2002 began to appear websites promoting networks online amigosen circles when the term is used to describe relationships in virtual communities, and became popular in 2003 with the arrival of sites such as MySpace or Xing. There are over 200 social networking sites, though Friendster has been one that has best used the technique of the circle of friends. The popularity of these sites grew rapidly and large companies have entered the space of social networking. For example, Google launched Orkut on January 22, 2004. Other search engines such as KaZaZZ! Yahoo and social networks created in 2005.

In these communities, an initial number of participants send messages to members of their own social network by inviting them to join the site. New participants repeat the process, increasing the total number of members and network links. The sites offer features such as automatic updating of the address book, visible profiles, the ability to create new links through introduction services and other forms of social networking online. Social networks can also be created around business relationships.

ICT tools to enhance the effectiveness of online social networks ('social software'), operating in three areas, "the 3Cs" cross-
§ Communication (help us to share knowledge).
§ Community (help us find and integrate communities).
§ Cooperation (help us to do things together).

Establishing contacts combined (blended networking) is an approach to social network that combines online and real world to create a mixture. A social network of people is combined if established through face to face events and online community. The two mix elements complement each other. See also Social computing.

Social networking on the Internet continue to advance rapidly, especially in what has been called Web 2.0 and Web 3.0, and among them, include a new phenomenon that seeks to assist users in their purchases on the Internet: social networking purchases. Social networking shopping try to become a place for consultation and purchase. A space where users can discuss any questions you have about the products you are interested, read and write reviews, vote for their favorite products, meet people with similar interests and, of course, buy the product in larger stores with a single click. This trend has a name, called Shopping 2.0.

Typology of social networking

There is no consensus among authors in proposing a specific type. In some sites apply the same type that once was used for portals, divide them into horizontal and vertical: Horizontal: seek to provide tools for interaction in general: Facebook, Google +, Hi5, Bebbo. Vertical by user type: target a specific audience, for example, professional Linkedin, MyCatSpace cat lovers, etc. Vertical by activity: those who promote a particular activity. YouTube Videos, Twitter microblogging, shopping and more.

Metrics (Measures) in social network analysis

Intermediation

The extent to which a node lies between other nodes in a network. This measure takes into account the connectivity of the node's neighbors, giving greater value to the nodes that connect groups. The measure reflects the number of persons a person connected indirectly through their direct links.

Connector

A bond can be called if their elimination connector causes the connecting points are transformed into different components of a graph.

Centrality

This measure gives a rough idea of ​​the social power of a node based on how well they "connect" it to the network. "Intermediation", "Closeness" and "Grade" are all measures of centrality.

Centralization

The difference between the number of links for each node divided by the maximum possible difference. A centralized network will have many of its links dispersed around one or a few nodal points, while a decentralized network is one in which there is little variation between the number of links each node possesses.

Closeness

The degree to which a person is close to all others in a network (directly or indirectly). It reflects the ability to access information through the "gossip network" of network members. Thus, the proximity is the inverse of the sum of the shortest distances between each individual and one from the others in the network. (See also: Proxemics). The shortest path is also known as the "geodesic distance".

Clustering coefficient

A measure of the probability that two people connected to a node to associate themselves. A higher clustering coefficient indicates a higher "exclusivism".

Cohesion

The degree to which actors are connected directly to each other by cohesive bonds. Groups are identified as 'cliques' if every individual is directly linked with each other, 'social circles' if there is less rigor in direct contact and this is vague, or blocks of structural cohesion if required accuracy.

Counting the number of links with other actors in the network. See also degree (graph theory).

(Individual level) Density

The degree of relationship of a defendant to know one another / proportion of ties between the mention of an individual. The network density or bulk density is the proportion of links in a network in relation to the total possible links (sparse versus dense networks)

Centrality of mediation flow

The degree to which a node contributes to the sum of the maximum flow between all pairs of us (excluding that node).

Eigenvector centrality (Auto Vector)

A measure of the importance of a node in a network. It assigns relative scores to all nodes in the network based on the principle that connections to nodes that have a high score contribute more to the score of the node.